Road Safety Risk Identification Driven by Abnormal Driving Behavior Data

Abstract

With the development of vehicle network technology, transportation companies have generated GPS-based driver behavior data for operating vehicles, providing potential for research on driver behavior and road safety risk perception. Research on road safety risk perception at the present stage generally involves fuzziness and subjectivity, the degree of risk is difficult to quantify, and big data-based road safety risk perception has not yet been used practically. To address this problem, this paper proposes a method of quantifying the degree of road safety risk based on the hierarchical analysis method and fuzzy comprehensive evaluation method under the drive of abnormal driving behavior data, constructs a scientific and reasonable urban road safety risk identification comprehensive evaluation model, and explores important factors affecting road safety. The research results have a certain reference effect for traffic management departments to accurately locate dangerous sections in the urban road network.